#!/usr/bin/python
# -*- coding: UTF-8 -*-

import time
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
from torch.autograd import Variable
import numpy as np

from semseg.loss import cross_entropy2d

affine_par = True


def outS(i):
    i = int(i)
    i = (i+1)/2
    i = int(np.ceil((i+1)/2.0))
    i = (i+1)/2
    return i
def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes, affine = affine_par)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes, affine = affine_par)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1,  dilation_ = 1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
        self.bn1 = nn.BatchNorm2d(planes,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        padding = 1
        if dilation_ == 2:
            padding = 2
        elif dilation_ == 4:
            padding = 4
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
                               padding=padding, bias=False, dilation = dilation_)
        self.bn2 = nn.BatchNorm2d(planes,affine = affine_par)
        for i in self.bn2.parameters():
            i.requires_grad = False
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4, affine = affine_par)
        for i in self.bn3.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride



    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class Classifier_Module(nn.Module):

    def __init__(self,dilation_series,padding_series,NoLabels):
        super(Classifier_Module, self).__init__()
        self.conv2d_list = nn.ModuleList()
        for dilation,padding in zip(dilation_series,padding_series):
            self.conv2d_list.append(nn.Conv2d(2048,NoLabels,kernel_size=3,stride=1, padding =padding, dilation = dilation,bias = True))

        for m in self.conv2d_list:
            m.weight.data.normal_(0, 0.01)


    def forward(self, x):
        out = self.conv2d_list[0](x)
        for i in range(len(self.conv2d_list)-1):
            out += self.conv2d_list[i+1](x)
        return out



class ResNet(nn.Module):
    def __init__(self, block, layers,n_classes):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64,affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation__ = 2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation__ = 4)
        self.layer5 = self._make_pred_layer(Classifier_Module, [6,12,18,24],[6,12,18,24],n_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
        #        for i in m.parameters():
        #            i.requires_grad = False

    def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion,affine = affine_par),
            )
        for i in downsample._modules['1'].parameters():
            i.requires_grad = False
        layers = []
        layers.append(block(self.inplanes, planes, stride,dilation_=dilation__, downsample = downsample ))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes,dilation_=dilation__))

        return nn.Sequential(*layers)
    def _make_pred_layer(self,block, dilation_series, padding_series,NoLabels):
        return block(dilation_series,padding_series,NoLabels)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        return x

class MS_Deeplab(nn.Module):
    def __init__(self,block,n_classes):
        super(MS_Deeplab,self).__init__()
        self.Scale = ResNet(block,[3, 4, 23, 3],n_classes)   #changed to fix #4

    def forward(self,x):
        input_size = x.size()[2]
        self.interp1 = nn.UpsamplingBilinear2d(size = (  int(input_size*0.75)+1,  int(input_size*0.75)+1  ))
        self.interp2 = nn.UpsamplingBilinear2d(size = (  int(input_size*0.5)+1,   int(input_size*0.5)+1   ))
        self.interp3 = nn.UpsamplingBilinear2d(size = (  outS(input_size),   outS(input_size)   ))
        out = []
        x2 = self.interp1(x)
        x3 = self.interp2(x)
        out.append(self.Scale(x))	# for original scale
        out.append(self.interp3(self.Scale(x2)))	# for 0.75x scale
        out.append(self.Scale(x3))	# for 0.5x scale

        x2Out_interp = out[1]
        x3Out_interp = self.interp3(out[2])
        # print('out[0].data.size():', out[0].data.size())
        # print('x2Out_interp.data.size():', x2Out_interp.data.size())
        temp1 = torch.max(out[0],x2Out_interp)
        out.append(torch.max(temp1,x3Out_interp))
        return out

def Res_Deeplab(n_classes=21):
    model = MS_Deeplab(Bottleneck,n_classes)
    return model

if __name__ == '__main__':
    n_classes = 21
    image_crop = 480
    model = Res_Deeplab(n_classes=n_classes)
    # model.init_vgg16()
    # 输入到deeplab中的图像数据宽高相同，如果crop大小不相同，那么大的部分增加，原图增加000，标签图增加IGNORE_LABEL
    # torch.nn.CrossEntropyLoss(ignore_index=IGNORE_LABEL)
    x = Variable(torch.randn(1, 3, image_crop, image_crop))
    y = Variable(torch.LongTensor(np.ones((1, image_crop, image_crop), dtype=np.int)))
    # print(x.shape)

    # ---------------------------pspnet模型运行时间-----------------------
    start = time.time()
    outs = model(x)
    end = time.time()
    print(end-start)

    # for out in outs:
    #     print out.data.shape

    interp = nn.UpsamplingBilinear2d(size=(image_crop, image_crop))
    out = outs[3]
    pred = interp(out)
    print('pred.data.size():', pred.data.size())
    print('out.data.size():', out.data.size())
    # print(pred)
    loss = cross_entropy2d(pred, y)
    print(loss)
